When people talk about big data, the conversation inevitably turns to how, well, BIG big data is: how much data is out there and how quickly it is growing. Size and speed are thought to signify importance. But most people don’t truly understand big data. Most of us don’t actually make enough of the data we already sit on.

Let’s remind ourselves what makes data big: it’s the four Vs:

Volume – how much data is out there

Variety – the many different formats that data comes in

Velocity – the speed at which new data is generated and added to the existing pool

Veracity – the quality of the data that’s gathered.

Each describes an attribute that makes it challenging to handle data. What none of them do is indicate how important that data is. Big data, if you like, is “fat” data. It comes with a lot of excess baggage that does not add “nutritional value”.

So what can organisations do to use data the right way?

Slim data is good data

More often than not, what we actually need is not data itself, but actionable insights: indicators that help us make decisions that are guided by more than simply our gut feeling. To get there, we have to “shave” data. That is, we have to clean what we have gathered and shed all the unnecessary bits and bytes that do not tell us anything. Technicians call this white noise. The majority of data in any sufficiently large data set is noise.

Context creates information

So what makes data useful? Data turns into information only when we start to contextualise it. Making connections is the oil of every inferential process. Think of it as a puzzle. If a piece of information can fill an empty spot, then we get a more complete picture. We gain knowledge that improves our ability to make good decisions.

Breaking barriers leads to value

Context finds its limits at borders. Borders exist from one platform to the other – for example Facebook to Google or TV to print. They exist between what we know about the minds of customers and the situations they buy in. Yet by far the biggest obstacle is the border that often exists between different organisational units. Data needs to flow freely in an organisation to generate value.

Your data doesn’t have to be big, your data-drive has to be

Big data itself has no intrinsic value. The question to ask is not how much data, but what you are trying to change and what data do you need to help you with that? Whether your data satisfies the 4V criteria above or not is of little consequence, as long as it tells you what you need to know.

Large scale implementation is not necessary; beta mode is

Even with a plan, one can invest a fortune in data warehouses and controlling processes without ever getting the value back that one hoped for. Data is rarely a silver bullet. Especially in the marketing communication space, the environment we operate in is extremely complex. Outcomes are very hard to predict. Taking cues from Silicon Valley, companies need to be more willing to experiment. The trade-off is to accept failure on a smaller scale more often in order to make progress and avoid bigger mistakes down the road. We should be in beta mode, testing things.

Essentially, the job of communications professionals is to relate to our stakeholders. Unlike product development, we don’t just need to understand a particular need and our audiences’ habits around that need. We need to understand them in their entirety, as human beings. We need to know what they want, why they want it, where they want it, when they want it, how they want it. We also need to anticipate why and when they change their minds, and what that means for us.

To know all of that, we need to ask questions. However, people get tired of being asked to participate in market research. We need to make better use of what people tell us voluntarily every day, simply because they are part of social networks, use apps and are in our CRM databanks. We have a duty to make the most out of this data to deliver the most engaging communications possible.

The skills required to make use of data are only 50% technical and statistical aptitude. The other 50% is sound thinking: formulating good hypotheses based on what we know and what we hope to gain. The challenge doesn’t really lie in big data but in analytical thinking: the ability to recognise and solve problems in sensible ways by using available information.

Perhaps that is also the reason for why analytics is searched for about 15 times more often on Google than big data. Ironically, using big data tells us that it is not what we need to talk about. It’s time that we change the conversation.